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Nkengue MJ, Zeng X, Koehl L, Tao X, Dassonville F, Dumont N, Ye-Lehmann S, Akwa Y, Ye H. An intelligent garment for long COVID-19 real-time monitoring. Comput Biol Med 2024; 181:109067. [PMID: 39182371 DOI: 10.1016/j.compbiomed.2024.109067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 08/13/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.
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Affiliation(s)
- Marc Junior Nkengue
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France; Univ. Lille, Ecole Centrale Lille, F-59000, Lille, France.
| | - Xianyi Zeng
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Ludovic Koehl
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Xuyuan Tao
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - François Dassonville
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Nicolas Dumont
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Shixin Ye-Lehmann
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
| | - Yvette Akwa
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
| | - Hanwen Ye
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
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2
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Attallah O. Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets. Sci Rep 2024; 14:20637. [PMID: 39232043 PMCID: PMC11375051 DOI: 10.1038/s41598-024-69954-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 08/12/2024] [Indexed: 09/06/2024] Open
Abstract
Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial-textural-frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model's performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
- Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, 21937, Egypt.
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3
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Sharkas M, Attallah O. Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform. Sci Rep 2024; 14:6914. [PMID: 38519513 PMCID: PMC10959971 DOI: 10.1038/s41598-024-56820-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called "Color-CADx" for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training-testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.
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Affiliation(s)
- Maha Sharkas
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt
| | - Omneya Attallah
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt.
- Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
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4
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Wen W, Zhang H, Wang Z, Gao X, Wu P, Lin J, Zeng N. Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion. Comput Biol Med 2024; 171:108210. [PMID: 38417383 DOI: 10.1016/j.compbiomed.2024.108210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/08/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.
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Affiliation(s)
- Weimin Wen
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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Nishikimi R, Nakano M, Kashino K, Tsukada S. Variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:19-28. [PMID: 38390581 PMCID: PMC10879006 DOI: 10.1016/j.cvdhj.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
Background For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation. Objective The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters. Methods The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model. Results Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. Conclusion The proposed method has the potential to become an alternative to computationally expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.
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Affiliation(s)
- Ryo Nishikimi
- NTT Communication Science Laboratories, Atsugi, Japan
| | - Masahiro Nakano
- NTT Communication Science Laboratories, Atsugi, Japan
- NTT Basic Research Laboratories, Atsugi, Japan
| | - Kunio Kashino
- NTT Communication Science Laboratories, Atsugi, Japan
- NTT Basic Research Laboratories, Atsugi, Japan
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Chorney W, Wang H, Fan LW. AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19. Comput Biol Med 2024; 168:107743. [PMID: 38000247 DOI: 10.1016/j.compbiomed.2023.107743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/24/2023] [Accepted: 11/20/2023] [Indexed: 11/26/2023]
Abstract
The novel coronavirus caused a worldwide pandemic. Rapid detection of COVID-19 can help reduce the spread of the novel coronavirus as well as the burden on healthcare systems worldwide. The current method of detecting COVID-19 suffers from low sensitivity, with estimates of 50%-70% in clinical settings. Therefore, in this study, we propose AttentionCovidNet, an efficient model for the detection of COVID-19 based on a channel attention convolutional neural network for electrocardiograms. The electrocardiogram is a non-invasive test, and so can be more easily obtained from a patient. We show that the proposed model achieves state-of-the-art results compared to recent models in the field, achieving metrics of 0.993, 0.997, 0.993, and 0.995 for accuracy, precision, recall, and F1 score, respectively. These results indicate both the promise of the proposed model as an alternative test for COVID-19, as well as the potential of ECG data as a diagnostic tool for COVID-19.
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Affiliation(s)
- Wesley Chorney
- Computational Engineering, Mississippi State University, Mississippi State, 39762, USA.
| | - Haifeng Wang
- Industrial and Systems Engineering, Mississippi State University, Mississippi State, 39762, USA.
| | - Lir-Wan Fan
- Pediatrics/Newborn Medicine, University of Mississippi Medical Center, Mississippi State, 39216, USA.
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7
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Wu L, Guo S, Han L, Song X, Zhao Z, Cekderi AB. Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network. Health Inf Sci Syst 2023; 11:33. [PMID: 37538261 PMCID: PMC10393931 DOI: 10.1007/s13755-023-00237-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Myocarditis is cardiac damage caused by a viral infection. Its result often leads to a variety of arrhythmias. However, rapid and reliable identification of myocarditis has a great impact on early diagnosis, expedited treatment, and improved patient survival rates. Therefore, a novel strategy for the autonomous detection of myocarditis is suggested in this work. First, the improved quantum genetic algorithm (IQGA) is proposed to extract the optimal features of ECG beat and heart rate variability (HRV) from raw ECG signals. Second, the backpropagation neural network (BPNN) is optimized using the adaptive differential evolution (ADE) algorithm to classify various ECG signal types with high accuracy. This study examines analogies among five different ECG signal types: normal, abnormal, myocarditis, myocardial infarction (MI), and prior myocardial infarction (PMI). Additionally, the study uses binary and multiclass classification to group myocarditis with other cardiovascular disorders in order to assess how well the algorithm performs in categorization. The experimental results demonstrate that the combination of IQGA and ADE-BPNN can effectively increase the precision and accuracy of myocarditis autonomous diagnosis. In addition, HRV assesses the method's robustness, and the classification tool can detect viruses in myocarditis patients one week before symptoms worsen. The model can be utilized in intensive care units or wearable monitoring devices and has strong performance in the detection of myocarditis.
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Affiliation(s)
- Lei Wu
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
| | - Shuli Guo
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xiaowei Song
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
| | - Zhilei Zhao
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
| | - Anil Baris Cekderi
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
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8
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Attallah O. RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection. Biomimetics (Basel) 2023; 8:417. [PMID: 37754168 PMCID: PMC10527565 DOI: 10.3390/biomimetics8050417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/28/2023] Open
Abstract
Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth of subject-matter specialists in this area. Nonetheless, a major generalization obstacle is posed by the existence of small discrepancies between various classes of paddy diseases. Numerous studies have used features taken from a single deep layer of an individual complex DL construction with many deep layers and parameters. All of them have relied on spatial knowledge only to learn their recognition models trained with a large number of features. This study suggests a pipeline called "RiPa-Net" based on three lightweight CNNs that can identify and categorize nine paddy diseases as well as healthy paddy. The suggested pipeline gathers features from two different layers of each of the CNNs. Moreover, the suggested method additionally applies the dual-tree complex wavelet transform (DTCWT) to the deep features of the first layer to obtain spectral-temporal information. Additionally, it incorporates the deep features of the first layer of the three CNNs using principal component analysis (PCA) and discrete cosine transform (DCT) transformation methods, which reduce the dimension of the first layer features. The second layer's spatial deep features are then combined with these fused time-frequency deep features. After that, a feature selection process is introduced to reduce the size of the feature vector and choose only those features that have a significant impact on the recognition process, thereby further reducing recognition complexity. According to the results, combining deep features from two layers of different lightweight CNNs can improve recognition accuracy. Performance also improves as a result of the acquired spatial-spectral-temporal information used to learn models. Using 300 features, the cubic support vector machine (SVM) achieves an outstanding accuracy of 97.5%. The competitive ability of the suggested pipeline is confirmed by a comparison of the experimental results with findings from previously conducted research on the recognition of paddy diseases.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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9
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Hassan A, Elhoseny M, Kayed M. A novel and accurate deep learning-based Covid-19 diagnostic model for heart patients. SIGNAL, IMAGE AND VIDEO PROCESSING 2023; 17:1-8. [PMID: 37362230 PMCID: PMC10197036 DOI: 10.1007/s11760-023-02561-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 06/28/2023]
Abstract
Using radiographic changes of COVID-19 in the medical images, artificial intelligence techniques such as deep learning are used to extract some graphical features of COVID-19 and present a Covid-19 diagnostic tool. Differently from previous works that focus on using deep learning to analyze CT scans or X-ray images, this paper uses deep learning to scan electro diagram (ECG) images to diagnose Covid-19. Covid-19 patients with heart disease are the most people exposed to violent symptoms of Covid-19 and death. This shows that there is a special, unclear relation (until now) and parameters between covid-19 and heart disease. So, as previous works, using a general diagnostic model to detect covid-19 from all patients, based on the same rules, is not accurate as we prove later in the practical section of our paper because the model faces dispersion in the data during the training process. So, this paper aims to propose a novel model that focuses on diagnosing accurately Covid-19 for heart patients only to increase the accuracy and to reduce the waiting time of a heart patient to perform a covid-19 diagnosis. Also, we handle the only one existed dataset that contains ECGs of Covid-19 patients and produce a new version, with the help of a heart diseases expert, which consists of two classes: ECGs of heart patients with positive Covid-19 and ECGs of heart patients with negative Covid-19 cases. This dataset will help medical experts and data scientists to study the relation between Covid-19 and heart patients. We achieve overall accuracy, sensitivity and specificity 99.1%, 99% and 100%, respectively. Supplementary Information The online version contains supplementary material available at 10.1007/s11760-023-02561-8.
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Affiliation(s)
- Ahmed Hassan
- Faculty of Science, Beni-Suef University, Beni-Suef, 62511 Egypt
| | - Mohamed Elhoseny
- Faculty of Computers and Information, Mansoura University, Mansoura, 35516 Egypt
| | - Mohammed Kayed
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, 62511 Egypt
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10
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Pham BT, Le PT, Tai TC, Hsu YC, Li YH, Wang JC. Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction. SENSORS (BASEL, SWITZERLAND) 2023; 23:2993. [PMID: 36991703 PMCID: PMC10051525 DOI: 10.3390/s23062993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 06/19/2023]
Abstract
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.
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Affiliation(s)
- Bach-Tung Pham
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Phuong Thi Le
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Tzu-Chiang Tai
- Department of Computer Science and Information Engineering, Providence University, Taichung City 43301, Taiwan
| | - Yi-Chiung Hsu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Yung-Hui Li
- AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
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11
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Attallah O. RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2023; 233:104750. [PMID: 36619376 PMCID: PMC9807270 DOI: 10.1016/j.chemolab.2022.104750] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 05/28/2023]
Abstract
Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering & Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt
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12
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Ribeiro P, Marques JAL, Rodrigues PM. COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020198. [PMID: 36829692 PMCID: PMC9952817 DOI: 10.3390/bioengineering10020198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease.
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Affiliation(s)
- Pedro Ribeiro
- CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
| | | | - Pedro Miguel Rodrigues
- CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
- Correspondence:
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13
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Gomes JC, de Santana MA, Masood AI, de Lima CL, dos Santos WP. COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis. Med Biol Eng Comput 2023; 61:1057-1081. [PMID: 36662377 PMCID: PMC9854415 DOI: 10.1007/s11517-023-02773-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023]
Abstract
In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.
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Affiliation(s)
| | | | - Aras Ismael Masood
- Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
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14
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GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13020171. [PMID: 36672981 PMCID: PMC9857608 DOI: 10.3390/diagnostics13020171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/05/2023] Open
Abstract
One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP's superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time.
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15
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Attallah O. MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning. Digit Health 2023; 9:20552076231180054. [PMID: 37312961 PMCID: PMC10259124 DOI: 10.1177/20552076231180054] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/18/2023] [Indexed: 06/15/2023] Open
Abstract
Objective Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named "Monkey-CAD" that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features' sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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16
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Attallah O, Aslan MF, Sabanci K. A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods. Diagnostics (Basel) 2022; 12:2926. [PMID: 36552933 PMCID: PMC9776637 DOI: 10.3390/diagnostics12122926] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022] Open
Abstract
Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh-Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT's reduced features obtained from the three DL models. Additionally, the three DL models' PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
| | - Muhammet Fatih Aslan
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, Turkey
| | - Kadir Sabanci
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, Turkey
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17
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Nguyen T, Pham HH, Le KH, Nguyen AT, Thanh T, Do C. Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks. PLoS One 2022; 17:e0277081. [PMID: 36331942 PMCID: PMC9635737 DOI: 10.1371/journal.pone.0277081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.
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Affiliation(s)
- Thao Nguyen
- College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
| | - Hieu H. Pham
- College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
- VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam
| | - Khiem H. Le
- College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
| | - Anh-Tu Nguyen
- College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
| | - Tien Thanh
- Pulmonology, Vinmec International Hospital, Hanoi, Vietnam
| | - Cuong Do
- College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
- * E-mail:
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18
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OWAE-Net: Covid-19 detection from ECG images using Deep Learning and Optimized Weighted Average Ensemble Technique. INTELLIGENT SYSTEMS WITH APPLICATIONS 2022. [PMCID: PMC9676168 DOI: 10.1016/j.iswa.2022.200154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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Liu S, Cai T, Tang X, Zhang Y, Wang C. COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention. Comput Biol Med 2022; 149:106065. [PMID: 36081225 PMCID: PMC9433340 DOI: 10.1016/j.compbiomed.2022.106065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/07/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022]
Abstract
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.
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Affiliation(s)
- Shangwang Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China.
| | - Tongbo Cai
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Xiufang Tang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Yangyang Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Changgeng Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
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20
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Izonin I, Kutucu H, Singh KK. Smart systems and data-driven services in healthcare. Comput Biol Med 2022; 158:106074. [PMID: 36109250 DOI: 10.1016/j.compbiomed.2022.106074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/27/2022]
Abstract
The modern development of Medicine and Healthcare is primarily based on the automation of various processes to support the correct and timely medical decisions. A doctor or other medical staff's fast, accurate, reliable diagnosis, prevention, and treatment processes are the keys to quality patient care. This aim can be achieved by developing reliable, intelligent Healthcare and medicine service systems for various purposes. The Special Issue covers science-intensive real-time solutions that underlie smart systems and data-driven services in Healthcare and Medicine.
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Affiliation(s)
- Ivan Izonin
- Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, 79013, Ukraine.
| | - Hakan Kutucu
- Department of Software Engineering, Karabuk University, Karabuk, 78050, Turkey.
| | - Krishna Kant Singh
- Amity School of Engineering and Technology, Amity University, Uttar Pradesh, India.
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21
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Laddha S, Mnasri S, Alghamdi M, Kumar V, Kaur M, Alrashidi M, Almuhaimeed A, Alshehri A, Alrowaily MA, Alkhazi I. COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study. Diagnostics (Basel) 2022; 12:1880. [PMID: 36010231 PMCID: PMC9406661 DOI: 10.3390/diagnostics12081880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.
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Affiliation(s)
- Saloni Laddha
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Sami Mnasri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
- Department of Computer Science, ISSAT of Gafsa, University of Gafsa, Gafsa 2112, Tunisia
| | - Mansoor Alghamdi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Malek Alrashidi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Abdullah Almuhaimeed
- The National Centre for Genomics Technologies and Bioinformatics, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Ali Alshehri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Majed Abdullah Alrowaily
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Ibrahim Alkhazi
- College of Computers & Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia;
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22
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Attallah O, Samir A. A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices. Appl Soft Comput 2022; 128:109401. [PMID: 35919069 PMCID: PMC9335861 DOI: 10.1016/j.asoc.2022.109401] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 12/30/2022]
Abstract
The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral–temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral–temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral–temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral–temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral–temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral–temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
| | - Ahmed Samir
- Department of Radiodiagnosis, Faculty of Medicine, University of Alexandria, Egypt
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23
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Agrawal A, Chauhan A, Shetty MK, P GM, Gupta MD, Gupta A. ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput Biol Med 2022; 146:105540. [PMID: 35533456 PMCID: PMC9055384 DOI: 10.1016/j.compbiomed.2022.105540] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/26/2022] [Accepted: 04/15/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19. METHOD We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects. RESULTS ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%. CONCLUSION So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.
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Affiliation(s)
| | | | | | - Girish M. P
- Department of Cardiology, GIPMER, Delhi, India
| | | | - Anubha Gupta
- SBILab, Department of ECE, IIIT-Delhi, Delhi, India,Corresponding author
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24
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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25
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Attallah O. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. BIOSENSORS 2022; 12:299. [PMID: 35624600 PMCID: PMC9138764 DOI: 10.3390/bios12050299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/24/2022] [Indexed: 06/01/2023]
Abstract
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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Sobahi N, Sengur A, Tan RS, Acharya UR. Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection. Comput Biol Med 2022; 143:105335. [PMID: 35219186 PMCID: PMC8858432 DOI: 10.1016/j.compbiomed.2022.105335] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible. METHOD For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer. RESULTS A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database.
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Affiliation(s)
- Nebras Sobahi
- King Abdulaziz University, Department of Electrical and Computer Engineering, Jeddah, Saudi Arabia.
| | - Abdulkadir Sengur
- Firat University, Technology Faculty, Electrical and Electronics Engineering Department, Elazig, Turkey
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore and Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu—A Case Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2048294. [PMID: 35309835 PMCID: PMC8930239 DOI: 10.1155/2022/2048294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/04/2022] [Accepted: 02/15/2022] [Indexed: 12/24/2022]
Abstract
This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.
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Attallah O. A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images. Digit Health 2022; 8:20552076221092543. [PMID: 35433024 PMCID: PMC9005822 DOI: 10.1177/20552076221092543] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/21/2022] [Indexed: 12/14/2022] Open
Abstract
The accurate and rapid detection of the novel coronavirus infection, coronavirus is very important to prevent the fast spread of such disease. Thus, reducing negative effects that influenced many industrial sectors, especially healthcare. Artificial intelligence techniques in particular deep learning could help in the fast and precise diagnosis of coronavirus from computed tomography images. Most artificial intelligence-based studies used the original computed tomography images to build their models; however, the integration of texture-based radiomics images and deep learning techniques could improve the diagnostic accuracy of the novel coronavirus diseases. This study proposes a computer-assisted diagnostic framework based on multiple deep learning and texture-based radiomics approaches. It first trains three Residual Networks (ResNets) deep learning techniques with two texture-based radiomics images including discrete wavelet transform and gray-level covariance matrix instead of the original computed tomography images. Then, it fuses the texture-based radiomics deep features sets extracted from each using discrete cosine transform. Thereafter, it further combines the fused texture-based radiomics deep features obtained from the three convolutional neural networks. Finally, three support vector machine classifiers are utilized for the classification procedure. The proposed method is validated experimentally on the benchmark severe respiratory syndrome coronavirus 2 computed tomography image dataset. The accuracies attained indicate that using texture-based radiomics (gray-level covariance matrix, discrete wavelet transform) images for training the ResNet-18 (83.22%, 74.9%), ResNet-50 (80.94%, 78.39%), and ResNet-101 (80.54%, 77.99%) is better than using the original computed tomography images (70.34%, 76.51%, and 73.42%) for ResNet-18, ResNet-50, and ResNet-101, respectively. Furthermore, the sensitivity, specificity, accuracy, precision, and F1-score achieved using the proposed computer-assisted diagnostic after the two fusion steps are 99.47%, 99.72%, 99.60%, 99.72%, and 99.60% which proves that combining texture-based radiomics deep features obtained from the three ResNets has boosted its performance. Thus, fusing multiple texture-based radiomics deep features mined from several convolutional neural networks is better than using only one type of radiomics approach and a single convolutional neural network. The performance of the proposed computer-assisted diagnostic framework allows it to be used by radiologists in attaining fast and accurate diagnosis.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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Bassiouni MM, Hegazy I, Rizk N, El-Dahshan ESA, Salem AM. Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports. CIRCUITS, SYSTEMS, AND SIGNAL PROCESSING 2022; 41:5535-5577. [PMID: 35615749 PMCID: PMC9122255 DOI: 10.1007/s00034-022-02035-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 05/16/2023]
Abstract
One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.
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Affiliation(s)
- Mahmoud M. Bassiouni
- Egyptian E-Learning University (EELU), 33 El-messah Street, Eldokki, El-Giza, 11261 Egypt
| | - Islam Hegazy
- Faculty of Computer and Information Science, Ain Shams University, Abbassia, Cairo, 11566 Egypt
| | - Nouhad Rizk
- Computer Science Department, Houston University, Houston, USA
| | - El-Sayed A. El-Dahshan
- Egyptian E-Learning University (EELU), 33 El-messah Street, Eldokki, El-Giza, 11261 Egypt
- Department of Physics, Faculty of Science, Ain Shams University, Cairo, 11566 Egypt
| | - Abdelbadeeh M. Salem
- Faculty of Computer and Information Science, Ain Shams University, Abbassia, Cairo, 11566 Egypt
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